Image segmentation typically involves separating object regions of an image from background regions of the image. Many different approaches for segmenting an image have been proposed, including thresholding, region growing, and watershed transform based image segmentation processes. The segmentation results of such processes may be used for a wide variety of different applications, including object extraction for object description or recognition. In general, noise reduces the accuracy with which an image segmentation process can segment objects from background regions.
Text-like objects in digital images that are captured by camera-equipped handheld devices (e.g., digital cameras, cellular telephones, and personal digital assistants) often are degraded by nonuniform illumination and blur. The presence of these artifacts significantly degrades the overall appearance quality of the reproduced digital images. In addition, such degradation adversely affects OCR (optical character recognition) accuracy.
What are needed are apparatus and methods that are capable of segmenting and enhancing document images in ways that are robust to text font size, blur level and noise.